1. Chapter 13 Quality Control and Improvement COMPLETE BUSINESS STATISTICSby AMIR D. ACZEL & JAYAVEL SOUNDERPANDIAN 7th edition. Prepared by Lloyd Jaisingh, Morehead State University McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.

2. Using Statistics W. Edwards Deming Instructs Statistics and Quality The x-bar Chart The R Chart and the s Chart The p Chart The c Chart The x Chart Quality Control and Improvement 13 13-2

3. Determine when to use control charts Create control charts for sample means, ranges and standard deviations Create control charts for sample proportions Create control charts for the number of defectives Draw Pareto charts using spreadsheet templates Draw control charts using spreadsheet templates LEARNING OBJECTIVES 13 After studying this chapter you will be able to: 13-3

4. A control chart is a time plot of a statistic, such as a sample mean, range, standard deviation, or proportion, with a center line and upper and lower control limits. The limits give the desired range of values for the statistic. When the statistic is outside the bounds, or when its time plot reveals certain patterns, the process may be out of control. A process is considered in statistical control if it has no assignable causes, only natural variation. UCL LCL Center Line Time Value This point is out of the control limits 33 33 13-3 Statistics and Quality 13-4

5. Value Time Value Time Process is in control Process mean varies over time: process is out of control Control Charts 13-5

6. Control Charts (Continued) 13-6

7. Pareto diagram A Pareto diagram is a bar chart of the various problems in production and their percentages, which must add to 100%. Pareto Diagrams – Using the Template A Pareto chart helps to identify the most significant problems and thus one can concentrate on their solutions rather than waste time and resources on unimportant causes. 13-7

8. Finished products are grouped in lots before being shipped to customers. The lots are numbered, and random samples from these lots are inspected for quality. Such checks are made before the lots are shipped and after the lots arrive at their destination. The random samples are measured to find out which and how many items do not meet specifications A lot is rejected whenever the sample mean exceeds or falls below some pre-specified limit. Finished products are grouped in lots before being shipped to customers. The lots are numbered, and random samples from these lots are inspected for quality. Such checks are made before the lots are shipped and after the lots arrive at their destination. The random samples are measured to find out which and how many items do not meet specifications A lot is rejected whenever the sample mean exceeds or falls below some pre-specified limit. Acceptance Sampling 13-8

9. For attribute data, the lot is rejected when the number of defectives or non-conforming items in the sample exceeds a pre-specified limit. Acceptance sampling does not improve quality by itself. It simply removes bad lots. To improve quality, it is necessary to control the production process itself, removing any assignable causes and striving to reduce the variation in the process. For attribute data, the lot is rejected when the number of defectives or non-conforming items in the sample exceeds a pre-specified limit. Acceptance sampling does not improve quality by itself. It simply removes bad lots. To improve quality, it is necessary to control the production process itself, removing any assignable causes and striving to reduce the variation in the process. Acceptance Sampling 13-9

10. Six Sigma is a further innovation, beyond Deming’s work, in the field of quality assurance and control. The purpose of Six Sigma is to push defect levels below a certain specified threshold. Six Sigma helps to improve quality. The key to Six Sigma is a precise definition of the production process with accurate measurements and valid collection of data. Six Sigma is a further innovation, beyond Deming’s work, in the field of quality assurance and control. The purpose of Six Sigma is to push defect levels below a certain specified threshold. Six Sigma helps to improve quality. The key to Six Sigma is a precise definition of the production process with accurate measurements and valid collection of data. Six Sigma 13-10

11. It also involves detailed analysis to measure the relationships and causality of key factors in the production process. Experimental Design is used to identify these key factors. Strict control of the production process is exercised. Any variations are corrected, and the process is further monitored as it goes on line. The essence of Six Sigma is the statistical methods described in this chapter. It also involves detailed analysis to measure the relationships and causality of key factors in the production process. Experimental Design is used to identify these key factors. Strict control of the production process is exercised. Any variations are corrected, and the process is further monitored as it goes on line. The essence of Six Sigma is the statistical methods described in this chapter. Six Sigma 13-11

12. n A 2 c 4 21.8800.7979 31.0230.8862 40.7290.9213 50.5770.9400 60.4830.9515 70.4190.9594 80.3730.9650 90.3370.9693 100.3080.9727 150.2230.9823 200.1800.9869 250.1530.9896 n A 2 c 4 21.8800.7979 31.0230.8862 40.7290.9213 50.5770.9400 60.4830.9515 70.4190.9594 80.3730.9650 90.3370.9693 100.3080.9727 150.2230.9823 200.1800.9869 250.1530.9896 13-4 The X-Bar Chart: A Control Chart for the Process Mean 13-12

13. Tests for assignable causes: One point beyond 3  (3s) Nine points in a row on one side of the center line Six points in a row steadily increasing or decreasing Fourteen points in a row alternating up and down Two out of three points in a row beyond 2  (2s) Four out of five points in a row beyond 1  (1s) Fifteen points in a row within 1  (1s) of the center line Eight points in a row on both sides of the center line, all beyond 1  (1s) Tests for assignable causes: One point beyond 3  (3s) Nine points in a row on one side of the center line Six points in a row steadily increasing or decreasing Fourteen points in a row alternating up and down Two out of three points in a row beyond 2  (2s) Four out of five points in a row beyond 1  (1s) Fifteen points in a row within 1  (1s) of the center line Eight points in a row on both sides of the center line, all beyond 1  (1s) The X-Bar Chart: A Control Chart for the Process Mean (Continued) 13-13

14. Time Value 11 11 22 22 33 33 Test 1:One value beyond 3  (3s) Test 1:One value beyond 3  (3s) Time Value 11 11 22 22 33 33 Test 2:Nine points in a row on one side of the center line. Test 2:Nine points in a row on one side of the center line. Tests for Assignable Causes 13-14

15. Time Value 11 11 22 22 33 33 Test 3:Six points in a row steadily increasing or decreasing. Test 3:Six points in a row steadily increasing or decreasing. Time Value 11 11 22 22 33 33 Test 4:Fourteen points in a row alternating up and down. Test 4:Fourteen points in a row alternating up and down. Tests for Assignable Causes (Continued) 13-15

16. Time Value 11 11 22 22 33 33 Test 5:Two out of three points in a row beyond 2  (2s) Test 5:Two out of three points in a row beyond 2  (2s) Time Value 11 11 22 22 33 33 Test 6:Four out of five points in a row beyond 1  (1s) Test 6:Four out of five points in a row beyond 1  (1s) Tests for Assignable Causes (Continued) 13-16

17. Time Value 11 11 22 22 33 33 Test 7:Fifteen points in a row within 1  (1s) of the center line. Test 7:Fifteen points in a row within 1  (1s) of the center line. Time Value 11 11 22 22 33 33 Test 8:Eight points in a row on both sides of the center line, all beyond 1  (1s) Test 8:Eight points in a row on both sides of the center line, all beyond 1  (1s) Tests for Assignable Causes (Continued) 13-17

18. X-bar Chart: Example 13-1 – Using the Template 13-18

19. X-bar Chart: Example 13-1(continued) – Using the Template Note: The X-bar chart cannot be interpreted unless the R or s chart has been examined and is in control. 13-19

20. X-bar Chart: Example 13-1(continued) – Using Minitab Note: The X-bar chart cannot be interpreted unless the R or s chart has been examined and is in control. 13-20

21. n D 3 D 4 B 3 B 4 203.26703.267 302.57502.568 402.28202.266 502.11502.089 602.0040.0301.970 70.0761.9240.1181.882 80.1361.8640.1851.815 90.1841.8160.2391.761 100.2231.7770.2841.716 150.3481.6520.4281.572 200.4141.5860.5101.490 250.4591.5410.5651.435 n D 3 D 4 B 3 B 4 203.26703.267 302.57502.568 402.28202.266 502.11502.089 602.0040.0301.970 70.0761.9240.1181.882 80.1361.8640.1851.815 90.1841.8160.2391.761 100.2231.7770.2841.716 150.3481.6520.4281.572 200.4141.5860.5101.490 250.4591.5410.5651.435 13-5 The R Chart and s Chart 13-21

22. R Chart: Example 13-1 using the Template The process range seems to be in control. 13-22

23. s Chart: Example 13-1 using the Template The process standard deviation seems to be in control. 13-23

24. Example 13-2 using the Template 13-24

25. Example 13-2 using the Template - Continued 13-25

26. Example 13-2 using the Template - Continued Based on the x-bar, R, and s charts, the process seems to be in control. 13-26

27. Example 13-2 using Minitab 13-27

28. Example 13-2 using Minitab 13-28

29. Example 13-2 using Minitab Based on the x-bar, R, and s charts, the process seems to be in control. 13-29

30. 13-6 The p Chart: Proportion of Defective Items 13-30

31. 13-6 The p Chart: Proportion of Defective Items – Using the Template for Example 13-3 Process is out of control – Two points fall outside the control limit 13-31

32. 13-6 The p Chart: Proportion of Defective Items – Using Minitab for Example 13-3 Process is out of control – Two points fall outside the control limit 13-32

33. 13-7 The c Chart: (Defects Per Item) 13-33

34. The c Chart: Example 13-4 using the Template Observe that one observation is outside the upper control limit, indicating that the process may be out of control. The general downward trend should be investigated. 13-34

35. The c Chart: Example 13-4 using Minitab Observe that one observation is outside the upper control limit, indicating that the process may be out of control. The general downward trend should be investigated. 13-35

36. 13-8 The x Chart Sometimes we are interested in controlling the process mean, but our observations come so slowly from the production process that we cannot aggregate them into groups. In such case we may consider an x chart. An x-chart is a chart for the raw values of the variable in question. The chart is effective if the variable has an approximate normal distribution. The bounds are 3 standard deviations from the mean of the process. 13-36

37. 13-8 The x Chart for Example 13-3 – Using Minitab NOTE: The X-Chart Is same as the Individual chart in Minitab 13-37

38. 13-8 The x Chart for Example 13-4 – Using Minitab 13-38